Evaluating the effect of the accountability audit of natural resources on carbon emissions reduction in China

The accountability audit of natural resources (AANR) is a major institutional arrangement for advancing the construction of an ecological civilization in China. Based on the panel data of 271 cities in China from 2005 to 2017, this paper investigates the relationship between the AANR and carbon dioxide (CO2) emissions using a multi-period difference-in-differences (DID) model. The results show that AANR significantly increases the CO2 emission reduction rate by 0.009 units at the 5% significance level. The results still hold after a series of robustness tests. Given all else being equal, this significant effect is 0.001. Further analyses show that AANR improves pilot cities’ CO2 emission reduction rates mainly by enhancing their green innovation capability. The mediating effect of cities’ green technology innovation capability accounts for 96.00%, while the AANR’s direct effect only accounts for 4.00%. The AANR has significantly positive effects of 0.017% and 0.029% for western cities and cities with high fiscal pressure at the 5% and 1% significance levels, respectively. Therefore, strengthening AANR implementation by enhancing the mediating efficiency of cities’ green technology innovations and implementing dynamically differentiated AANR policies in Chinese meso-cities will contribute to the achievement of China’s carbon peaking and carbon neutrality targets

Approximated Prompt Tuning for Vision-Language Pre-trained Models

Prompt tuning is a parameter-efficient way to deploy large-scale pre-trained models to downstream tasks by adding task-specific tokens. In terms of vision-language pre-trained (VLP) models, prompt tuning often requires a large number of learnable tokens to bridge the gap between the pre-training and downstream tasks, which greatly exacerbates the already high computational overhead. In this paper, we revisit the principle of prompt tuning for Transformer-based VLP models and reveal that the impact of soft prompt tokens can be actually approximated via independent information diffusion steps, thereby avoiding the expensive global attention modeling and reducing the computational complexity to a large extent. Based on this finding, we propose a novel Approximated Prompt Tuning (APT) approach towards efficient VL transfer learning. To validate APT, we apply it to two representative VLP models, namely ViLT and METER, and conduct extensive experiments on a bunch of downstream tasks. Meanwhile, the generalization of APT is also validated on CLIP for image classification. The experimental results not only show the superior performance gains and computation efficiency of APT against the conventional prompt tuning methods, e.g., +6.6% accuracy and -64.62% additional computation overhead on METER, but also confirm its merits over other parameter-efficient transfer learning approaches

Complex Field-based Fusion Network for Human Activities Classification With Radar

In the context of assisted living, human activity recognition (HAR) is of increased importance to maintain people at home living independently longer. Compared with directly using spectrograms (μD) or range profiles (RP) as inputs for classification discarding either range or explicit Doppler information, range-Doppler-surface (RDS) can more fully represent the information contained in the observed activities. However, because the data are collected from different activities, people, and locations, the RDS has a requires non-trivial adjustments for pre-processing as discussed in this paper to maintain the number of points in the RDS to fixed integer. Although it has improved performance (92%) compared to CNN (90%), this algorithm also discards phase information as most algorithms do in HAR with radar. In contrast, the phase information of the range-Doppler domain, although its performance was not the best (88%), it had no obvious weakness in the recognition of all the movements. Our proposed complex field-based fusion network (CFFN) combines the amplitude and phase which improves both the accuracy of classification (94%) as well as accelerating training time by 12.5%

Role of corporate governance in Internet firms during the 2000 shakeout.

This paper examines the association between Internet firm performance and corporate governance (CG) mechanisms, namely Board size, proportion of independent directors and duality, and if these same CG mechanisms affect the likelihood that an Internet firm will be delisted during the period 1996-2002, in relation to the Internet bubble burst in Spring 2000

Liquid Crystal@Nanosilver Catalytic Amplification—Aptamer Trimode Biosensor for Trace Pb2+

Liquid crystals (LCs) are a very important display material. However, the use of LC, especially LC-loaded nanoparticles, as a catalyst to amplify the analytical signal and coupled with specific aptamer (Apt) as a recognition element to construct a highly sensitive and selective three-mode molecular spectral assay is rarely reported. In this article, five LCs, such as cholesteryl benzoate (CB), were studied by molecular spectroscopy to indicate the liquid crystal nanoparticles in the system, and highly catalytic and stable CB loaded-nanosilver (CB@AgNPs) sol was prepared. The slope procedure was used to study the catalysis of the five LCs and CB@AgNPs on the new indicator reaction between AgNO3 and sodium formate (Fo) to produce silver nanoparticles (AgNPs) with a strong surface plasmon resonance absorption (Abs) peak at 450 nm, a resonance Rayleigh scattering (RRS) peak at 370 nm and a surface enhanced Raman scattering (SERS) peak at 1618 cm−1 in the presence of molecular probes. By coupling the new CB@AgNPs catalytic indicator reaction with the Apt reaction, a new CB@AgNPs catalytic amplification-SERS/RRS/Abs trimode biosensoring platform was constructed for detecting inorganic pollutants, such as Pb2+, Cd2+, Hg2+ and As3+

Vertical Gold Nanowires Stretchable Electrochemical Electrodes

Conventional electrodes produced from gold or glassy carbon are outstanding electrochemical platforms for biosensing applications due to their chemical inertness and wide electrochemical window, but are intrinsically rigid and planar in nature. Hence, it is challenging to seamlessly integrate them with soft and curvilinear biological tissues for real-time wearable or implantable electronics. In this work, we demonstrate that vertically gold nanowires (v-AuNWs) possess an enokitake-like structure, with the nanoparticle (head) on one side and nanowires (tail) on the opposite side of the structure, and can serve as intrinsically stretchable, electrochemical electrodes due to the stronger nanowire-elastomer bonding forces preventing from interfacial delamination under strains. The exposed head side of the electrode comprising v-AuNWs can achieve a detection limit for H2O2 of 80 μM, with a linear range of 0.2–10.4 mM at 20% strain, with a reasonably high sensitivity using chronoamperometry. This excellent electrochemical performance in the elongated state, in conjunction with low-cost wet-chemistry fabrication, demonstrates that v-AuNWs electrodes may become a next-generation sensing platform for conformally integrated, in vivo biodiagnostics